In the financial stock market, a sequence of prices obtained from the share market with respect to the time series is usually examined. Generally, time series in finance, particularly shows importance in predicting investment in today's share market. Since there are too many factors such as public opinions, general economic conditions, or political events, vulnerability in the economy are directly or indirectly reflects on the evolution of financial time series. The desire of the investor is to predict the future stock prices neglecting whether the investor is a long-term investor or a day-trader. A major challenge is to develop and design an efficient predictive model that guides investors to make appropriate decisions. In this research work, Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) is developed to overcome such disputes and contributing an efficient technique for predicting the future stock prices financially. In addition to the model, cross entropy is calculated using a Mutual Information feature selection model to minimize the optimization problems that create the time complexity in the system. The proposed LSTM-RNN has achieved best accuracy of 61.33% of prediction accuracy compared to state-of-the-art techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.